Trust-based task unloading method

Abstract

The invention discloses a trust-based task unloading method. The trust-based task unloading method is divided into two parts. The method comprises the steps that 1, Internet of Things devices with edge service calculation requirements in a network are clustered, all the Internet of Things devices in a cluster can communicate with an unmanned aerial vehicle, and devices outside the cluster can transmit tasks to the devices in the cluster in a multi-hop mode and then unload the tasks to the unmanned aerial vehicle; the clustering mode can reduce the unmanned flight cost and expand the range of edge services; since the task outside the cluster only needs to be transmitted to any device in the cluster, overlarge data load of a single cluster head can be avoided. 2, a trust evaluation mechanism based on backtracking analysis is provided, active trust based on the unmanned aerial vehicle is introduced, trust evaluation is carried out on the Internet of Things equipment, and therefore the effectiveness of task unloading is guaranteed. In combination with a trust-based task transmission routing protocol, reliable equipment is selected for task relay forwarding or task unloading, smooth completion of a calculation task is ensured, and the packet loss rate of a network is reduced.

Description

technical field [0001] The invention belongs to the field of task offloading of mobile edge computing, in particular to a trust-based task offloading method. Background technique [0002] With the rapid development of the Internet of Things, the number of IoT devices deployed on the edge network is increasing, and a large amount of valuable data is generated. By processing and calculating the data generated in the edge network, valuable information can be extracted from the original perception data, reducing the data size and greatly reducing the load and computing pressure on the cloud. Processing a large amount of data requires high computing power, while the computing power provided by IoT devices is relatively weak and cannot meet the computing needs. The rise of task offloading technologies in edge networks provides an effective solution. A large number of scholars have studied and built edge computing platforms for task offloading. Among them, the most researched is...

AI Technical Summary

Problems solved by technology

In this drone-assisted scenario, since each IoT device needs to offload its tasks to the drone, there are the following disadvantages: (1) The energy consumption and cost of the drone are too high

Obviously, the longer the drone flies, the more energy it consumes and the higher the cost

(2) The reliability of task offloading cannot be guaranteed

There are attacks from malicious devices in the network, and the devices will discard some or all of the data packets during the transmission process, resulting in the task not being successfully transmitted to the UAV for edge computing, making it impossible for the device to receive the calculation results in a timely and effective manner

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